CN110456704A - A kind of storing grain method in tall and big room of closing a position - Google Patents
A kind of storing grain method in tall and big room of closing a position Download PDFInfo
- Publication number
- CN110456704A CN110456704A CN201910750726.XA CN201910750726A CN110456704A CN 110456704 A CN110456704 A CN 110456704A CN 201910750726 A CN201910750726 A CN 201910750726A CN 110456704 A CN110456704 A CN 110456704A
- Authority
- CN
- China
- Prior art keywords
- grain
- binning
- station
- warehouse
- tall
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Storage Of Harvested Produce (AREA)
Abstract
The embodiment of the present invention discloses a kind of storing grain method in tall and big room of closing a position, which comprises obtains the data parameters of warehouse, door, grain to be stored and binning equipment；The binning equipment grain outlet attainable position in the warehouse is determined according to the data parameters, determines the grain heap pattern of each point in warehouse；Determine in the warehouse can working path, by it is described can working path be divided into m can operation station, and set outlet station；Based on it is described can operation station and it is described outlet station position, a plurality of grain grain storage path is obtained by ant group algorithm；According to predeterminated target, optimal grain grain storage path is chosen；In this way, by ant group algorithm calculate in warehouse it is each it is described can the probability that is shifted to the outlet station of operation hole location, then according to predeterminated target, choose optimal grain grain storage path, so that binning machine binning amount is big, and variation call amount is few when carrying out grain storage in warehouse, and grain face is smooth, smooth grain face workload is less.
Description
Technical field
The present invention relates to a kind of storing grain methods, more particularly to a kind of storing grain method in tall and big room of closing a position.
Background technique
It is the main storehouse type that China invests to build in recent years, the research to this storehouse type for the tall and big room of closing a position of grain storage
Emphasis essentially consists in grain storage technology, management system and relevant device and its safety regulation for operations.The dress of many enterprise developments at present
Storehouse machine increases driving device, has all made good improvement in terms of conveying distance, height, binning efficiency, safety, but has lacked
A kind of few grain binning technique efficiently, orderly, conveniently, safe, for instructing grain depot staff standardization, efficient binning,
So that binning machine is moved in warehouse, number is few, and binning machine and operating personnel keep certain safe distance, fills after grain storage
Storehouse machine binning amount is big, and variation call amount is few, and grain face is smooth, and smooth grain face workload is less.
Summary of the invention
In order to solve the above technical problems, the embodiment of the present invention provides a kind of storing grain method in tall and big room of closing a position, and can make
In grain storage, binning machine moves that number is few in warehouse in tall and big room of closing a position, binning machine and operating personnel keep certain safety away from
From binning machine binning amount is big after grain storage, and variation call amount is few, and grain face is smooth, and smooth grain face workload is less.
In order to achieve the above object, the technical solution of the embodiment of the present invention is achieved in that
The embodiment of the present invention provides a kind of storing grain method in tall and big room of closing a position, which comprises
Obtain the data parameters of warehouse, door, grain to be stored and binning equipment；
The binning equipment grain outlet attainable position in the warehouse is determined according to the data parameters, determines storehouse
The grain heap pattern of each point in room；
Determine in the warehouse can working path, by it is described can working path be divided into m can operation station, and set
Export station, wherein the m is integer, and m >=1；
Based on it is described can operation station and it is described outlet station position, a plurality of grain grain storage road is obtained by ant group algorithm
Diameter；
According to predeterminated target, optimal grain grain storage path is chosen.
In embodiments of the present invention, it is described based on it is described can operation station and it is described outlet station position, pass through ant colony
The method that algorithm obtains a plurality of grain grain storage path are as follows:
Generation scale is the initial ant colony of m, and m ant is randomly placed on that m is described can be on operation station；
Calculate the probability that every ant is moved to the outlet station；
It is moved to the path when outlet station according to every ant, obtains the Pareto forward position of every ant；
According to Pareto forward position disaggregation, binning volume and grain face flatness, obtain it is each it is described can be new between operation station
Transition probability matrix.
In embodiments of the present invention, the method in the Pareto forward position is obtained are as follows:
According to formula: Min y=w1*1/y1+w2*y2+w3*y3；
Wherein, the y indicates tall and big room binning technique multiple-objection optimization evaluation function of closing a position；
The w1, the w2With the w3Respectively represent binning volume, in grain face flatness and storehouse operation number of stations power
Weight, the w1=0.6, the w2=0.3, the w3=0.1.
The y1For the binning volume of the n-th station, the n≤m；
y2Indicate grain face flatness and y2∈ [0,1]；
y3Indicate the number of stations of binning machine operation in storehouse.
In embodiments of the present invention, the y1Pass through formula:
It obtains；
Wherein, the vniIt is the binning amount of i-th of process of the n-th station；
The k is according to the operation quantity of the n-th station of the structure determination of close a position room and binning machine, and k >=1.
In embodiments of the present invention, the y2Pass through formula:
It obtains；
Wherein, the xniIt is the grain face height for i-th of grain heap that the n-th station heap goes out；
The k indicates all the lowest point of the n-th station institute heap grain face and the quantity on peak, k >=1.
In embodiments of the present invention, the y3Pass through formula:
It obtains；
Wherein, the len is the length in warehouse, and the wid is the width in warehouse；
Described loc_x, loc_y are the position of door, and the dr_wid is the width of door, and the dr_ht is door
Height dr_ht；
The ma_min is the forearm of binning machine, and the ma_max is the maximal dilation length of binning machine, the ma_
The maximum that h1 is the revolution height of binning machine, the ma_h2 is binning machine can throw grain height, the ma_tail is binning machine
Tail length, the wheelspan that the ma_wid is binning machine, the wheelbase that the ma_len is binning machine.
In embodiments of the present invention, described according to predeterminated target, the method for choosing optimal grain grain storage path are as follows:
Judge whether evolutionary generation meets；
As do not met, repeat it is described based on it is described can operation station and the outlet station position, pass through ant group algorithm
A plurality of grain grain storage path is obtained, until evolving terminates, exports optimal path.
The embodiment of the present invention provides a kind of storing grain method in tall and big room of closing a position, which comprises obtain warehouse, door,
The data parameters of grain and binning equipment to be stored；Determine the binning equipment grain outlet in institute according to the data parameters
Attainable position in warehouse is stated, determines the grain heap pattern of each point in warehouse；Determine in the warehouse can working path, by institute
State can working path be divided into m can operation station, and set outlet station, wherein the m is integer, and m >=1；Base
In it is described can operation station and it is described outlet station position, a plurality of grain grain storage path is obtained by ant group algorithm；According to pre-
It sets the goal, chooses optimal grain grain storage path；In this way, by ant group algorithm will in warehouse it is each it is described can operation hole location to institute
The probability calculation for stating outlet station transfer goes out, and then according to predeterminated target, optimal grain grain storage path is chosen, so that storehouse
In room when carrying out grain storage, binning machine binning amount is big after grain storage, and variation call amount is few, and grain face is smooth, smooth grain face workload
It is less.
Detailed description of the invention
Fig. 1 is the flow chart of the storing grain method in the tall and big room of closing a position that the embodiment of the present invention one provides；
Fig. 2 is the process of the method provided by Embodiment 2 of the present invention that a plurality of grain grain storage path is obtained by ant group algorithm
Figure；
Fig. 3 is the flow chart of the method in the selection optimal grain grain storage path that the embodiment of the present invention three provides.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description.
Embodiment one
The embodiment of the present invention provides a kind of storing grain method in tall and big room of closing a position, as shown in Figure 1, which comprises
Step S101: the data parameters of warehouse, door, grain to be stored and binning equipment are obtained；
Here, the data parameters for obtaining warehouse include: warehouse length, warehouse width and warehouse heap grain height.
The data parameters for obtaining door include: a center, gate-width degree, Men Gaodu, sealing plate height (when door mouth heap grain
When, need to install sealing plate additional).
Obtain the self-flowing angle of grain when the data parameters of the grain wait store include: the type of grain, bulk density and heap grain.
Obtain binning equipment data parameters include: forearm, maximal dilation length, revolution height, maximum can throw grain
Highly, the safe distance between the maximum elevation and grain heap and binning machine of tail length, wheelspan, wheelbase, equipment (generally takes 2
Rice).
Step S201: the binning equipment grain outlet attainable position in the warehouse is determined according to the data parameters
It sets, determines the grain heap pattern of each point in warehouse；
Here, according to the data parameters in the warehouse, the data parameters of door, the data parameters of binning equipment, initialization
Binning machine grain outlet all positions being likely to be breached in warehouse.
According to the self-flowing angle in the data parameters of grain to be stored, the grain heap formed on each possible position point is initialized
Pattern.
Step S301: determine in the warehouse can working path, by it is described can working path be divided into m can operation work
Position, and sets outlet station, wherein the m is integer, and m >=1；
Here, it is described can working path refer to according to door position and warehouse size plan binning machine automatically can operation road
Diameter, then can working path it is discrete for m can operation station, and set out the outlet station.
Step S401: based on it is described can operation station and it is described outlet station position, obtained by ant group algorithm a plurality of
Grain grain storage path；
Here, by ant group algorithm, calculate described in each can operation station be moved to it is described can be after operation station extremely
The route of the outlet station, that is, got a plurality of grain grain storage path.
Step S501: according to predeterminated target, optimal grain grain storage path is chosen.
Here, the predeterminated target includes the data such as binning volume, grain face flatness.
Embodiment two
Further, in embodiments of the present invention, as shown in Fig. 2, described can operation station and the outlet based on described in
The position of station, the method that a plurality of grain grain storage path is obtained by ant group algorithm are as follows:
Step S411: generating the initial ant colony that scale is m, and m ant is randomly placed on that m is described can be on operation station；
Step S412: the probability that every ant is moved to the outlet station is calculated；
Here, the formula that every ant is moved to the probability of the outlet station is calculated are as follows:
Wherein, the i indicates starting point, and the j indicates that terminal, the k indicate the node between beginning and end；
The ηij=1/dijIt is the inverse of two o'clock i, j road distance for visibility；
The τij(t) be time t when by i to j pheromones intensity；
The allowedkFor the node set still having not visited；
The α, the β are constant, are the weighted value of pheromones and visibility respectively.
Here, the dij=w_vo* grain increases volume (normalization)+w_fl*1/ (after pit depth normalization)；
The pit depth method for normalizing: Eta1=(vadd-min (vadd))/(max (vadd)-min (vadd)),
The smaller increased volume of expression grain heap of Eta1 value is smaller, and the vadd is increased grain volume；
The pit depth method for normalizing: Eta2=((max (fl)-fl)/(max (fl)-min (fl))), Eta2 is got over
The small deeper grain surface more out-of-flatness of expression pit, fl is pit depth；
The information content that information heuristic factor α reflection ant is accumulated during the motion, value is bigger, before ant selection
Pass by path a possibility that it is bigger, and the randomness searched for weakens, and α value is too small, then is easy to fall into ant group algorithm locally most
Excellent, α described here takes 1.
It is expected that heuristic factor β reflects relative importance of the ant in search process, size reflects ant optimization
The action intensity of apriority, certainty factor in the process, β value is bigger, and ant selects local shortest path in some partial points
A possibility that it is big, algorithm is easily trapped into local optimum, and β described here takes 4.
Pheromones volatilization factor ρ be ant pheromones evaporation rate, usual 0 < ρ≤1, when ρ is smaller, algorithm search space
Reduce, is easily trapped into local optimum, but algorithm is easy convergence, on the contrary then algorithm is not easy to fall into local optimum, and convergence reduces.
ρ described here takes 0.5.
Specifically, if the position where ant is not the finish node by doorway, ant needs to find next node
Possible station set；
According to current grain heap bottom surface situation, the subsequent possible work of binning machine under current location is calculated according to following principle
It selects: 1, current that the possible selection of next station cannot function as by the region that grain covers；2, current location front-wheel safety away from
It cannot function as the possible selection of next station from the region in range；3, when ant is in the lateral position perpendicular to door middle line
When, it can only transverse shifting in one direction；It 4, can only be mobile to doorway direction when ant is in door midline position.
Step S413: it is moved to the path when outlet station according to every ant, obtains the Pareto of every ant
Forward position；
Here, according to the generation the walked station of every ant, grain face maximum with binning volume be most smooth and storehouse in operation station
Number is at least target, calculates the Pareto forward position in the generation ant, finds the disaggregation in Pareto forward position.
Wherein, the method by obtaining the Pareto forward position are as follows:
According to formula: Min y=w1*1/y1+w2*y2+w3*y3；
Wherein, the y indicates tall and big room binning technique multiple-objection optimization evaluation function of closing a position；
The w1, the w2With the w3Respectively represent binning volume, in grain face flatness and storehouse operation number of stations power
Weight, the w1=0.6, the w2=0.3, the w3=0.1.
The y1For the binning volume of the n-th station, the n≤m；
y2Indicate grain face flatness and y2∈ [0,1]；
y3Indicate the number of stations of binning machine operation in storehouse.
Specifically, the y1Pass through formula:
It obtains；
Wherein, the vniIt is the binning amount of i-th of process of the n-th station；
The k is according to the operation quantity of the n-th station of the structure determination of close a position room and binning machine, and k >=1.
The y2Pass through formula:
It obtains；
Wherein, the xniIt is the grain face height for i-th of grain heap that the n-th station heap goes out；
The k indicates all the lowest point of the n-th station institute heap grain face and the quantity on peak, k >=1.
The y3Pass through formula:
It obtains；
Wherein, the len is the length in warehouse, and the wid is the width in warehouse；
Described loc_x, loc_y are the position of door, and the dr_wid is the width of door, and the dr_ht is door
Height dr_ht；
The ma_min is the forearm of binning machine, and the ma_max is the maximal dilation length of binning machine, the ma_
The maximum that h1 is the revolution height of binning machine, the ma_h2 is binning machine can throw grain height, the ma_tail is binning machine
Tail length, the wheelspan that the ma_wid is binning machine, the wheelbase that the ma_len is binning machine.
Step S414: according to Pareto forward position disaggregation, binning volume and grain face flatness, obtain it is each it is described can operation station
Between new transition probability matrix.
Embodiment three
Further, described according to predeterminated target as shown in figure 3, in embodiments of the present invention, choose optimal grain grain storage
The method in path are as follows:
Step S511: judge whether evolutionary generation meets, wherein the evolutionary generation refers to the number of algorithm iteration；
Step S512: as do not met, repeat it is described based on it is described can operation station and the outlet station position, pass through
Ant group algorithm obtains a plurality of grain grain storage path, until evolving terminates, exports optimal path.
The above is only the preferred embodiment of the present invention, it is noted that above-mentioned preferred embodiment is not construed as pair
Limitation of the invention, protection scope of the present invention should be defined by the scope defined by the claims..For the art
For those of ordinary skill, without departing from the spirit and scope of the present invention, several improvements and modifications can also be made, these change
It also should be regarded as protection scope of the present invention into retouching.
Claims (7)
1. a kind of storing grain method in tall and big room of closing a position, which is characterized in that the described method includes:
Obtain the data parameters of warehouse, door, grain to be stored and binning equipment；
The binning equipment grain outlet attainable position in the warehouse is determined according to the data parameters, is determined in warehouse
The grain heap pattern of each point；
Determine in the warehouse can working path, by it is described can working path be divided into m can operation station, and set outlet
Station, wherein the m is integer, and m >=1；
Based on it is described can operation station and it is described outlet station position, a plurality of grain grain storage path is obtained by ant group algorithm；
According to predeterminated target, optimal grain grain storage path is chosen.
2. the storing grain method in the tall and big room of closing a position of one kind according to claim 1, which is characterized in that described to be made based on described
The position of industry station and the outlet station, the method that a plurality of grain grain storage path is obtained by ant group algorithm are as follows:
Generation scale is the initial ant colony of m, and m ant is randomly placed on that m is described can be on operation station；
Calculate the probability that every ant is moved to the outlet station；
It is moved to the path when outlet station according to every ant, obtains the Pareto forward position of every ant；
According to Pareto forward position disaggregation, binning volume and grain face flatness, each transfer that can be new between operation station is obtained
Probability matrix.
3. the storing grain method in the tall and big room of closing a position of one kind according to claim 1, which is characterized in that before obtaining the Pareto
The method on edge are as follows:
According to formula: Min y=w1*1/y1+w2*y2+w3*y3；
Wherein, the y indicates tall and big room binning technique multiple-objection optimization evaluation function of closing a position；
The w1, the w2With the w3Respectively represent binning volume, in grain face flatness and storehouse operation number of stations weight, institute
State w1=0.6, the w2=0.3, the w3=0.1.
The y1For the binning volume of the n-th station, the n≤m；
y2Indicate grain face flatness and y2∈ [0,1]；
y3Indicate the number of stations of binning machine operation in storehouse.
4. the storing grain method in the tall and big room of closing a position of one kind according to claim 3, which is characterized in that the y1Pass through formula:
It obtains；
Wherein, the vniIt is the binning amount of i-th of process of the n-th station；
The k is according to the operation quantity of the n-th station of the structure determination of close a position room and binning machine, and k >=1.
5. the storing grain method in the tall and big room of closing a position of one kind according to claim 3, which is characterized in that the y2Pass through formula:
It obtains；
Wherein, the xniIt is the grain face height for i-th of grain heap that the n-th station heap goes out；
The k indicates all the lowest point of the n-th station institute heap grain face and the quantity on peak, k >=1.
6. the storing grain method in the tall and big room of closing a position of one kind according to claim 3, which is characterized in that the y3Pass through formula:
It obtains；
Wherein, the len is the length in warehouse, and the wid is the width in warehouse；
Described loc_x, loc_y are the position of door, and the dr_wid is the width of door, and the dr_ht is the height of door
dr_ht；
The ma_min is the forearm of binning machine, and the ma_max is the maximal dilation length of binning machine, the ma_h1 is
Binning machine revolution height, the ma_h2 be binning machine maximum can throw grain height, the tail portion that the ma_tail is binning machine
Length, the wheelspan that the ma_wid is binning machine, the wheelbase that the ma_len is binning machine.
7. the storing grain method in the tall and big room of closing a position of one kind according to claim 1, which is characterized in that described according to predetermined mesh
Mark, the method for choosing optimal grain grain storage path are as follows:
Judge whether evolutionary generation meets；
As do not met, repeat it is described based on it is described can operation station and the outlet station position, obtained by ant group algorithm
A plurality of grain grain storage path exports optimal path until evolving terminates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910750726.XA CN110456704A (en) | 2019-08-14 | 2019-08-14 | A kind of storing grain method in tall and big room of closing a position |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910750726.XA CN110456704A (en) | 2019-08-14 | 2019-08-14 | A kind of storing grain method in tall and big room of closing a position |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110456704A true CN110456704A (en) | 2019-11-15 |
Family
ID=68486601
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910750726.XA Pending CN110456704A (en) | 2019-08-14 | 2019-08-14 | A kind of storing grain method in tall and big room of closing a position |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110456704A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527965A (en) * | 2016-01-04 | 2016-04-27 | 江苏理工学院 | Route planning method and system based on genetic ant colony algorithm |
| US20170017884A1 (en) * | 2014-06-23 | 2017-01-19 | International Business Machines Corporation | Solving vehicle routing problems using evolutionary computing techniques |
| CN106971245A (en) * | 2017-03-30 | 2017-07-21 | 广东工业大学 | A kind of determining method of path and system based on improvement ant group algorithm |
-
2019
- 2019-08-14 CN CN201910750726.XA patent/CN110456704A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170017884A1 (en) * | 2014-06-23 | 2017-01-19 | International Business Machines Corporation | Solving vehicle routing problems using evolutionary computing techniques |
| CN105527965A (en) * | 2016-01-04 | 2016-04-27 | 江苏理工学院 | Route planning method and system based on genetic ant colony algorithm |
| CN106971245A (en) * | 2017-03-30 | 2017-07-21 | 广东工业大学 | A kind of determining method of path and system based on improvement ant group algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 廖敏 等: "基于蚁群算法的高大平房仓装仓工艺研究", 《粮油食品科技》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106403950B (en) | A kind of robot palletizer method for planning track based on reference information | |
| CN109903049A (en) | A kind of block chain transaction data storage method, device, equipment and storage medium | |
| CN107480103B (en) | A kind of optimization method of pump-storage generator Pump Failure operating condition distributor closing law | |
| CN108764579B (en) | Storage multi-robot task scheduling method based on congestion control | |
| CN102682185A (en) | Single wind turbine wind power prediction method | |
| CN110533096A (en) | The DNA of multiverse algorithm based on K-means cluster stores Encoding Optimization | |
| CN101957945A (en) | Method and device for optimizing goods loading of container | |
| CN109597304A (en) | Die storehouse Intelligent partition storage method based on artificial bee colony algorithm | |
| CN109677830A (en) | A kind of intensive warehousing system method for optimizing resource allocation of four-way shuttle vehicle type | |
| CN101916301B (en) | Three-dimensional spatial data adaptive pre-scheduling method based on spatial relationship | |
| CN110456704A (en) | A kind of storing grain method in tall and big room of closing a position | |
| CN103246924A (en) | Genetic algorithm based RFID system | |
| CN107341570A (en) | Reservoir filling phase runoff grading control power generation dispatching method under the conditions of a kind of random water | |
| CHANG et al. | Research on order picking optimization problem of automated warehouse | |
| CN107016080A (en) | A kind of high-efficiency network packet classification method | |
| CN104698838B (en) | Based on the fuzzy scheduling rule digging method that domain dynamic is divided and learnt | |
| Guimarães et al. | Scheduling wafer slicing by multi-wire saw manufacturing in photovoltaic industry: a case study | |
| CN110348045A (en) | A kind of shale gas drilling parameter optimization method based on modified sawtooth genetic algorithm | |
| Hamza et al. | Differential evolution with multi-constraint consensus methods for constrained optimization | |
| CN106026189B (en) | A kind of low-carbon Itellectualized uptown source lotus coordination optimizing method | |
| CN113344383B (en) | Energy-saving workshop scheduling method for distributed heterogeneous factory | |
| CN109635999A (en) | A kind of power station dispatching method looked for food based on population-bacterium and system | |
| Ma et al. | Travel time analysis for shuttle-based storage and retrieval system with middle input/output location | |
| CN102013038A (en) | Wireless sensor network service life optimizing genetic algorithm based on forward encoding strategy | |
| Yingde et al. | Dynamic slotting optimization based on skus correlations in a zone-based wave-picking system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191115 |
|
| RJ01 | Rejection of invention patent application after publication |